Conference Agenda

Continued glacier mass loss in High Mountain Asia impacts freshwater supply in and beyond the mountains. Previous studies have shown large spatial variations in glacier mass balance in this region, but the reasons for this variability are not well understood. We developed a new index based on satellite-derived surface characteristics to discriminate winter- and summer-accumulation type glaciers across High Mountain Asia. Combined with the existing mass balance data, it is found that the accumulation type is closely related with accumulation type. Glacier regions that gain mass predominantly from summer snow have thinned on average nearly four times faster than those gaining most mass in winter (-0.43 ± 0.12 m water equivalent (w.e.) a^-1 vs -0.10 ± 0.06 m w.e. a^-1 from 2000 to 2018). The results highlight the importance of the seasonality of snowfall for the glacier mass budget emphasizing that accurate precipitation fields are paramount to quantify future glacier changes reliably in this region.

The monitoring and forecasting of global water cycle under climate changes indeed require enhancement of satellite remote sensing products in both of spatial resolution and accuracy. To strengthen the ability of microwave remote sensing in global water cycle studies and seek for new opportunities of satellite missions, we put forward research contents as follows in the second year of project implementation:

(1) Continuous L-Band Soil Moisture (SM) Datasets from SMOS and SMAP Observations

The Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) are two existing satellites capable of providing L-band observations at a global scale. Although both satellites have independently performed calibrations, there are some differences in brightness temperature (TB) between the two. Intercalibrations were conducted to develop a consistent SMOS-SMAP TB, then the multi-channel collaborative algorithm (MCCA), which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved, is adopted to develop a consistent L-band soil moisture dataset. Inter-comparison with other SM products (MT-DCA version 5, and DCA, SCA-H, and SCA-V from SMAP Level-3 products version 7) shows an analogous spatial pattern. The MCCA derived SM had the lowest ubRMSD (about 0.058 m3/m3) followed by DCA (0.061 m3/m3), and an overall Pearson’s correlation coefficient of 0.702 (DCA performed best with R=0.746) when evaluated against in situ observations from 19 dense soil moisture networks. The MCCA generates vegetation optical depth (VOD) at both vertical and horizontal polarization, which were found to have a good linearity with the live biomass and canopy height, though partial saturation exists in the relationship with live biomass of tropical forests but not canopy height. The polarization difference of VOD mainly located at densely vegetated and arid areas. It is important to note that this continuous L-band SM and polarized VOD dataset is expected to improve our understanding of the water-transport process in the soil-vegetation continuum.

(Submitted to Remote Sensing of Environment)

(2) Continuous X-Band Soil Moisture (SM) Datasets from FY-3 Series Observations

Long term SM data with stable and consistent quality are critical for global environment and climate change monitoring. SM products from L-band observations have proven to be optimal global estimations. Although X-band has a lower sensitivity to soil moisture than that of L-band, Chinese FengYun-3 series satellites (FY-3A/B/C/D) have provided sustainable and daily multiple SM products from X-band since 2008. This research developed a new global SSM product (NNsm-FY) from FY-3B MWRI from 2010 to 2019, transferred high accuracy of SMAP L-band to FY-3B X-band. The NNsm-FY shows good agreement with in-situ observations and SMAP product and has a higher accuracy than that of official FY-3B product. At selected dense in-situ networks, it is found that NNsm-FY has a relatively good performance with median CC of 0.66 and median ubRMSE of 0.046 m3/m3, With this new dataset, Chinese FY-3 satellites may play a larger role and provide opportunities of sustainable and longer-term soil moisture data record for hydrological study.

(Submitted to Scientific Data)

The main objective of the project is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a synergic and innovative way to integrate RS data from NRSCC and ESA into terrestrial system models for better quantifying the water cycle at the watershed/regional scale. The objective will be achieved through the following sub-objectives: i) Retrieval of key water cycle variables from multi-source RS data (WP1); ii) Development of real time RS LDAS to integrate RS data into terrestrial system models (WP2); iii) Calibration/validation of terrestrial system models using RS retrievals of key water cycle variables (WP3); iv) Parameter estimations for terrestrial system models based on the LDAS (WP3); v) Closing and quantifying the water cycle at the watershed/regional scale based on the LDAS (WP4).

Two LDAS will be developed in the project, one for the silk road endorheic river basins (LDAS_Silk) and one for EUROCORDEX-domain (LDAS_EU). LDAS_Silk will be based on the recently developed watershed system model and a common software for nonlinear and non-Gaussian land data assimilation (ComDA). LDAS_EU will be based on the recently developed Terrestrial System Modeling Platform (TSMP) and Parallel Data Assimilation Framework (PDAF). Multi-source RS data, from visible to thermal infrared and microwave, will be used to retrieve key ecohydrological variables, such as evapotranspiration (ET), snow coverage area (SCA), snow water equivalent (SWE), snow depth (SD), soil moisture (SM), lake and glacier extents, irrigation, and vegetation density and structure. These data will be used as forcing data, calibration and validation data, and for assimilation into the two LDAS.

In this presentation, the mid-term progress on the project will be reported.